Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa

Simple item page
dc.contributor.authorAbu, Mahamuda
dc.contributor.authorMvile, Benatus Norbert
dc.contributor.authorKalimenze, John Desderius
dc.date.accessioned2024-08-19T06:43:23Z
dc.date.available2024-08-19T06:43:23Z
dc.date.issued2024
dc.descriptionAbstract. Full text available at: https://link.springer.com/article/10.1007/s12665-024-11419-2
dc.description.abstractThe source of clastic sediments generally, can be traced to their source through provenance studies using the whole rock geochemistry of clastic sediments. However, the provenance of the Au-bearing stream sediments within the central parts of Tanzania is yet to be deciphered. Hence, in this study, to enhance exploration targeting, the source of the Au-bearing stream sediments was characterized using whole-rock geochemistry. The performance of linear regression (LR), decision tree (DT), and polynomial regression (PR) models as prediction models for the Au mineralization in the area, were also compared as additional Au exploration techniques worth exploring in the area. The weathering condition proxies, CIA, ICV, CIW, and PIA as well as discriminant diagrams suggest weakly to intensely weathered sediments. The values of SiO2/Al2O3 and K2O/Al2O3 are indicative of felsic source rocks rather than compositional maturity due to sediments reworking. From Th/Cr, Cr/Th, Th/U, La/Sc, and Th/Sc proxies, the Au-bearing stream sediments are sourced from felsic igneous rocks. These indications are corroborated by the correlation matrix assessment. However, Au is not sourced from the same source rocks as the host sediments due probably, to a prior depositional mixing of the sediments before subsequent transportation to their current depositional environment. With R2 (0.62), MAE (0.6035), MSE (0.6546), and RMSE (0.8091) for LR, R2 (1.0), MAE (0.7500), MSE (1.6273), and RMSE (1.2752) for DT, and R2 (1.0), MAE (2.6608), MSE (12.7840), and RMSE (3.5755), for PR. The LR model performs better in predicting the Au occurrence in the area.
dc.identifier.citationAbu, M., Mvile, B. N., & Kalimenze, J. D. (2024). Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa. Environmental Earth Sciences, 83(3), 105.
dc.identifier.doi10.1007/s12665-024-11419-2
dc.identifier.otherURL: https://link.springer.com/article/10.1007/s12665-024-11419-2
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4728
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofEnvironmental Earth Sciences
dc.subjectRock geochemistry
dc.subjectAu-bearing sediments
dc.subjectClastic sediments
dc.subjectRock geochemistry machine learning
dc.subjectStream sediments
dc.titleProvenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa
dc.typeArticle
oaire.citation.issue3
oaire.citation.volume83
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
John Desderius Kalimenze - Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models insight from whole-rock geochemistry central Tanzania, East Africa.pdf
Size:
11.78 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Journal Articles